Autotuning PID Controllers for Quadplane Hybrid UAV using Differential Evolution Algorithm - Pairan, Mohammad Fahmi; Shamsudin, Syariful Syafiq; Yaakub, Mohd Fauzi (2024)
The quadplane has become the favorite UAV platform for drone delivery. The dynamics and control of the quadplane UAV are extremely complex, unstable, and nonlinear due to its required capability to operate in multiple flight modes. The paper presents a method for autotuning attitude PID for a quadplane UAV using differential evolution (DE), X-Plane simulation, and neural network (NN)-based system identification. This study uses the DE algorithm and a radial basis function neural network (RBF NN) to create an autotuning PID and unified dynamics model for quadplanes, with the goal of improving their performance in delivery missions. The unified RBF model is then used for autotuning the PID controller using the DE algorithm. The proposed RBF-NN system identification can successfully predict the attitude dynamics of a quadplane in all flight modes with a coefficient of determination of R2 greater than 0.9 and a computing mean time of less than 5 ms. By iteratively modifying the control settings to achieve optimal performance, the DE algorithm replaces the requirement for manual PID tuning, which can be time-consuming and suboptimal. Comparing DE tuning to manual tuning, the results demonstrate a considerable improvement in quadplane roll and pitch performance of about 58.5%, 65.0%, and 74.0% in the overshoot, peak time, and rising time, respectively.